A Corner Detection Algorithm Using Anisotropic Gaussian Directional Derivatives Autocorrelation Matrix on Edge Contours

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A Corner Detection Algorithm Using Anisotropic Gaussian Directional Derivatives Autocorrelation Matrix on Edge Contours

更新时间：2025-07-09

- A Corner Detection Algorithm Using Anisotropic Gaussian Directional Derivatives Autocorrelation Matrix on Edge Contours
- Vol. 46, Issue 11, Pages: 91-97(2012)
- 作者机构：
  
  西安电子科技大学雷达信号处理国家重点实验室,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN973.3
- Online First：10 November 2012，
  
  Published：2012
- 稿件说明：
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A Corner Detection Algorithm Using Anisotropic Gaussian Directional Derivatives Autocorrelation Matrix on Edge Contours[J]. 2012, 46(11): 91-97.
DOI：

A Corner Detection Algorithm Using Anisotropic Gaussian Directional Derivatives Autocorrelation Matrix on Edge Contours[J]. 2012, 46(11): 91-97. DOI：

摘要

为了抑制图像边缘上的局部变化和噪声对角点检测的影响

提出了利用各向异性高斯方向导数自相关矩阵的角点检测算法.该算法首先利用Canny边缘检测器提取图像的边缘映射; 然后用各向异性高斯方向导数滤波器对输入图像进行平滑

对每个边缘像素

利用它与相邻像素之间方向导数的相关性构造自相关矩阵; 若边缘像素点的自相关矩阵所对应的归一化特征值的和是局部极大值

则标记该点为角点.与传统的基于轮廓的角点检测方法不同

文中提出的方法利用的是邻近像素的方向导数的相关信息

而不是轮廓曲线的曲率

因而具有更好的稳健性.实验结果表明:在无噪声和含噪声的条件下

该检测方法与已有的3种算法相比

平均配准角点数分别提高了7.4%和9.3%左右

平均定位误差分别降低了10%和15.2%左右.

Abstract

A new corner detection algorithm based on the anisotropic Gaussian directional derivatives(ANDDs)autocorrelation matrix on edge contours is proposed to suppress noise and local variation

and to detect corners effectively. Firstly

the edge map of an image is extracted by the Canny edge detector. Secondly

the input image is smoothed by the ANDD filters; autocorrelation matrices are constructed for each edge pixel by the directional derivatives correlation of the pixel and its surrounding pixels. Finally

the contour pixels with local maxima of the sum of the normalized eigenvalues are labeled as corners. The proposed algorithm is different from the traditional contour-based detectors

and it uses the intensity variation auto-information on contours and their surrounding pixels rather than the curvatures of the planar curves

hence has better robustness to noise. Experimental results and comparisons with several state-of-art algorithms in both the noise-free and noise cases show that the average matched corner numbers of the proposed algorithm increase by about 7.4 and 9.3 percent

respectively; and the average positioning errors reduce by about 10 and 15.2 percent

respectively.

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Keywords

references

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